class_docstr: Classifier based on BERT implementation.
docstr: Score the batch.
docstr_tok: ["Score","the","batch","."]
code: def score(self, batch):
      """
      Score the batch.
      """
      segment_idx = (batch.text_vec * 0).long()
      if self.sep_last_utt:
          batch_len = batch.text_vec.size(1)
          # find where [SEP] token is
          seps = (batch.text_vec == self.dict.end_idx).nonzero()
          if len(seps) > 0:
              for row in seps:
                  # set last utterance to segment 1
                  segment_idx[
                      row[0], list(range(row[1], batch_len))
                  ] = 1
          else:
              # only one utterance: everything after [CLS] token
              # should be segment 1
              segment_idx = (
                  batch.text_vec != self.dict.start_idx
              ).long()
      mask = (batch.text_vec != self.NULL_IDX).long()
      token_idx = batch.text_vec * mask
      return self.model(token_idx, segment_idx, mask)
  
imports: import os
  from collections import deque
  from typing import Optional
  import torch
  from parlai.agents.bert_ranker.bert_dictionary import BertDictionaryAgent
  from parlai.agents.bert_ranker.helpers import BertWrapper, MODEL_PATH
  from parlai.core.opt import Opt
  from parlai.core.params import ParlaiParser
  from parlai.core.torch_agent import History
  from parlai.core.torch_classifier_agent import TorchClassifierAgent
  from parlai.utils.io import PathManager
  from parlai.utils.misc import warn_once
  from parlai.zoo.bert.build import download
label_desc: Use image node_id 10 for calling the BertClassifierAgent obj's underlying member method code with example usage: obj.score(batch) and returns: self